Demand Model Research Articles

National survey reveals elastic price sensitivity for select equine veterinary services.

The objective of this study was to estimate the price elasticity of demand for 3 common equine veterinary services: vaccinations (a routine service), lameness examinations (an elective service), and emergency colic surgery (an urgent service). Data were collected via a nationwide online survey of horse owners from August 15 to September 11, 2023, eliciting their willingness to pay for each service. The link to the online survey was distributed through participating organizations' social media and email lists to the target audience of US residents aged ≥ 18 years who were financially responsible for at least 1 horse, pony, mule, or donkey. Statistical software was used to estimate the demand models. The survey received a total of 4,915 usable responses, with at least one response from every state in the US. Results revealed elastic demand for all 3 services and subsample analyses provided reassurance as to the robustness of the results, suggesting that quantity demanded of these services would decrease more in a relative sense as compared to the increase in price. Understanding the current and future trends of equine veterinary service demand is vital in ensuring the long-term financial sustainability of equine veterinary practices. Moreover, due to vast differences in the pricing structure of small animal and equine practice procedures, business models for these types of practices should be treated separately. Although it may seem counterintuitive for practice growth, the estimated price elasticity of demand suggests that strategic price increases could enhance profitability. While higher prices may reduce service volume, they could also lead to lower costs, potentially offsetting any revenue reduction. Moreover, the findings on elastic demand can also inform decisions regarding discounts, service bundles, and the development of tiered service offerings.

Charging Profile Modeling of Electric Trucks at Logistics Centers

The future charging requirements of electric trucks will lead to new demands on the power grid. In order not to slow the expansion of the charging infrastructure for electric trucks, the power grid must be strengthened for this purpose. However, due to the limited penetration of electric trucks in fleets to date, grid planners lack information on their time- and location-dependent charging demand. The question arises as to how the charging demand of electric trucks can be realistically taken into account in power grid simulations. This paper therefore presents a methodology that makes it possible to quantify the charging demand of electric trucks at typical charging locations and derives initial parameters for power system planning with electric trucks. For location-based charging demand modeling, the arrival and departure behavior of trucks at representative logistics centers is combined with mobility data and vehicle parameters. This allows the determination of time series-based charging demand. A charging demand analysis at five different logistics center types shows that that energy demand, peak load, and temporal behavior vary greatly depending on the center type. It is therefore advisable to take these different charging location types into account when designing the electricity grids.

Evaluation of Air Traffic Network Resilience: A UK Case Study

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

Refined probabilistic seismic demand modeling of high-speed railway bridge under near-fault hanging-wall effects and velocity pulse effects by a novel ground motion selection approach

Refined probabilistic seismic demand modeling of high-speed railway bridge under near-fault hanging-wall effects and velocity pulse effects by a novel ground motion selection approach

Resource supply and demand model of military settlements in the cold weapon era: case of Zhenbao Town, Ming Great Wall

As an important military defense project in Chinese history, the Ming Dynasty Great Wall and its associated military settlements played a crucial role in maintaining border security. A systematic analysis of how military settlements allocated resources based on external threats and geo-strategic needs is essential to understanding their settlements defense systems. However, this aspect has been relatively under-explored in existing research. Therefore, a resource allocation assessment model for Great Wall military settlements during the Cold Weapon era is established to facilitate a more scientific analysis and quantitative assessment of Military Defense Capabilities(MDC) and Military Requirements(MR). This study employs a combined Analytical Hierarchy Process (AHP) and Principal Component Analysis (PCA) approach to determine the weight of each indicator for both military defense capability and military requirement. Spatial clustering using K-means is conducted to visualize the distribution of these strengths and weaknesses. An Ordinary Least Squares (OLS) regression model links military requirements with defense capabilities, assessing the relationship between settlement locations and historical battle sites. Residual analysis is utilized to identify areas of resource over-allocation or under-allocation. The study reveals that the regional differences in military defense capabilities and requirements within the Zhenbao Town area during the Ming Dynasty correspond closely with the locations of frequent conflicts. This finding suggests that the distribution of defensive capabilities were strengthened in areas with challenging terrain, while the effectiveness and allocation of defense capabilities were determined by logistical support and transportation conditions. This study enhances our understanding of historical military strategies from a historical geography perspective and offers innovative insights for analyzing Great Wall settlement, moving beyond mere sensory intuition and historical experience.

The Role of Intermediaries in Selection Markets: Evidence from Mortgage Lending

Abstract We study the role of brokers in selection markets. We find broker-clients in the Canadian mortgage market are observationally different from branch-clients. They finance larger loans with more leverage and longer amortization. We build and estimate a model of mortgage demand to disentangle three possible explanations for these riskier product choices: (1) selection on observables, (2) unobserved borrower preferences for riskier loans, and (3) a causal effect of brokers. Although we find that brokers influence product choices, the main reason borrowers choose high-leverage products is unobserved preferences. Borrowers prefer larger loans and brokers facilitate qualification for them. (JEL G21, D12)

Optimization of a novel lateral energy dissipation system for cable-stayed bridge with short piers based on seismic vulnerability analysis

Cable-stayed bridges with short piers are susceptible to becoming the structural weak link in seismic resistance during strong earthquakes. Traditional lateral restraint systems, such as fixed and sliding bearing systems, may impose substantial lateral forces and displacement demands, affecting the entire bridge's seismic safety. A butterfly-shaped steel plate damper has been developed and integrated into a lateral energy dissipation system along with elastoplastic cables to address this. Given the significant computational challenges and the complexity of identifying the optimal parameters for the energy dissipation system, this paper combines the probabilistic seismic demand model (PSDM) with the response surface method (RSM) to propose the seismic vulnerability fitting optimization method (SVFOM). Based on SVFOM, utilizing a central composite design (CCD), 17 analysis scenarios that account for the parameter variations of the lateral energy dissipation system are established. By developing response surface functions for bearing damage (ys) and pier damage (yp) through the seismic response fitting of the structure across all scenarios, we determined the optimal parameters for the lateral energy dissipation system to minimize damage to both bearings and piers. The finite element model validation analysis, which demonstrated significant reductions in pier displacement and the probability of bearing damage, confirms the effectiveness of the SVFOM. Optimized parameters showed that the new lateral energy dissipation system can significantly enhance the lateral seismic performance of cable-stayed bridges with short piers. Compared to scenarios without energy dissipation and with lateral sliding bearings, they demonstrated that this optimized system reduces the relative displacement of the piers and girders by 56 % and the probability of complete bearing failure by 87 % while maintaining pier damage within acceptable limits. This optimized system offers a valuable reference for the seismic design of similar bridge structures.

ETHOS.ActivityAssure—an open-source validation framework for synthetic European activity profiles

The simulation of human behavior is an essential component in the domain of energy demand modeling. However, due to its diverse nature, it is often unclear whether a simulated behavior pattern is fitting to certain contexts. Combined with the poor availability of appropriate activity data, this makes proper validation of behavior models difficult. Existing validation approaches are limited and specialized to the respective use case, and are therefore not reusable or comparable. To address this issue, the new open-source framework ETHOS.ActivityAssure is presented for evaluation of generated activity profiles, supporting the validation of behavior models. For this purpose, an aggregated activity dataset is created from restricted European time use data and published to ensure reusability. Validation is conducted through a set of indicators and comparative plots, taking into account activity duration, frequency, and time. A categorization of person types and activities enables mapping to result categories commonly used in behavior models. The framework's capabilities are demonstrated on the residential demand model LoadProfileGenerator. The developed framework allows for consistent and reproducible validation of synthetic activity profiles targeting Europe, without requiring access to confidential data. This offers the opportunity to enhance both new and existing behavior models by identifying flaws, compare multiple modeling approaches, and thoroughly evaluate model quality for diverse target purposes.

Mathematical modeling of Ethiopia's energy demand by sectors and energy types, with forecasts for the next 30 years

Due to the scarcity of economic resources, it is vital to optimize everything so that the supply and demand lines intersect in an optimized quantity, with no shortage or surplus of a provided item or service. Energy supply contains both surplus and shortage, thus estimating the amount of projected energy demand is a key work that must be completed. The objective of this paper is generating a mathematical model based on the actual data for thirty years forecasting. To create a mathematical model using actual data from the last fifteen years, a model that can represent the past trend and be used for future forecasting. This study provides a general overview of Ethiopia's current energy requirement with different energy type as well as sector-specific energy demand and estimates for different economic growth scenarios up to 2052. This model was created using a linear regression polynomial fit through Origin graphic and analysis software, and an econometric model was also applied. GDP was utilized as an independent variable in the economic model to determine the trend of energy consumption. Another input-output model is also used for multilinear regression to evaluate the change of four variables, GDP, population growth, urbanization growth rate, and general inflation rate, which was quantitatively linked with total energy requirement using Weka software. The mathematical model developed through linear and multilinear regression has been validated by using a different assumption on GDP growth based on past growth rate as low, medium and high growth rate and using the mathematical formula to generate an energy demand trend that can be compared with the actual trend; as a result, all the mathematical model that are generated has been found to be valid for the purpose of the intended work. Based on the generated mathematical model and different GDP growth rate scenario as low, medium, business as usual (BAU) and high a future energy demand was forecasted up to 2052 for thirty years. The model's results can help energy planners ensure that the country's supply capacity keeps up with predicted energy demand growth.

An interlinked dynamic model of timber and carbon stocks in Japan's wooden houses and plantation forests

Carbon absorption in growing trees is an important element of a carbon-neutral society, and the long-term storage of carbon stocks is a crucial sustainability challenge. Previous studies have focused on either live-biomass carbon stocks in plantation forests or anthropogenic carbon stocks in man-made objects. For a comprehensive nature-based climate solution, an analytical framework, dataset, and scenario setup for modeling the interrelationship between timber supply and demand are required. This study developed an interlinked material flow analysis model in which the timber demand for wooden houses is connected with timber supply from managed plantation forestry. We demonstrate the model by quantifying both live-biomass and anthropogenic carbon stocks and their potentials in Japan. We compared multiple scenario-runs of the model for wooden house demands estimated by population change with varying combinations of house types, structures, and lifespans. Our results show that carbon stocks will reach a maximum amount of 1.1 billion t-C by 2050 in a scenario of high demand for wooden detached houses with lifespan extensions. On the other hand, we also found that the aging of plantation forests and their reduced carbon-stocking capacities appear inevitable in any scenario owing to the limited demand for timber. Notably, despite the widely different settings of the various scenarios, our results exhibited narrow variances in future potential carbon storage in Japan. This can be explained by the unique population characteristics and building demographics of Japan. These counterintuitive findings highlight the need for interrelated modeling of the forestry and construction sectors. Our model and its scope are versatile and applicable to other case study areas, estimation periods, and target materials.

The role of agriculture for achieving renewable energy-centered sustainable development objectives in rural Africa

Multi-dimensional and overlapping barriers to wellbeing severely affect many areas in rural sub-Saharan Africa. In the region, more than 90% of cropland is rainfed, less than one third of households have electricity, almost 60% of the population reports food insecurity, and more than 35% of the population lives below the international poverty line. Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of these challenges, slowing and potentially reversing development. Thus, there is a strong need for multi-sector interventions across multiple levels, from national policies, to regional and river catchment-scale planning, to local planning and investment. To implement such actions, it is key not only to assess technological solutions and their investment needs, but also to appraise their feasibility and implementation potential (from both a policy and a financial point of view). Here, we implement a modelling platform (RE4AFAGRI platform), which soft-links bottom-up process-based water and energy demand and techno-economic infrastructure assessment models (WaterCROP, M-LED, OnSSET) into a multi-node, national Nexus-extended Integrated Assessment Model (MESSAGEix-Nexus) for supply and investment assessment. The results of our analysis shed light on the role of water and energy demand in the agricultural sector for jointly affecting infrastructure and investment requirements for achieving rural sustainable development objectives. We find that scenarios with increased ambition in expanding irrigation and agricultural productivity result in improved diffusion and economic feasibility of infrastructure to provide universal energy access while supporting productive uses of energy. Moreover, we conduct business model analysis to appraise the framework conditions and micro and macro determinants that can ensure feasibility of investment and uptake of small-scale infrastructure, crucial for rural development. Altogether, our research demonstrates how integrated modelling with an explicit focus on Nexus interlinkages can represent the enabling role and the business conditions for renewable energy input in agriculture to become a leverage of rural sustainable development. In turn, important policy and investment-relevant insights can be derived.

Development of Predictive Models for Energy Demand and CO2 Emission Trends for Households in Makurdi Metropolis

This study aimed at developing predictive models for energy demand and CO2 emission trends for households in Makurdi metropolis. As part of the study, households were randomly selected to represent a reasonable spread. Four major emission sources: electricity; transportation; solid waste and cooking fuels were considered, and data on the sources were obtained using records from relevant departments as well as questionnaires, surveys, and interviews of the occupants. This data was analyzed and used to calculate the CO2 emissions of the households using IPCC standard guidelines and formulae. The CO2 emission and energy demand predictive models were also developed using Design Expert 13. The ANOVA for the CO2 emission and energy demand shows a significant model (Significance F value of ≤ 0.0001) and a high R-squared value of 0.9981 and 0.9802 respectively. This indicates the adequacy of the models, and they can therefore be used to predict the CO2 and energy demand of households in Makurdi metropolis. The limitation remains the imbalance in living standards across the respective areas of the metropolis which definitely impacts on the demand and use of energy. It will be useful for policy makers and implementers in ensuring sustainable energy demand planning and CO2 emissions mitigation.

Assessment of catchment water resources allocation under climate change in Luwombwa sub-catchment, Zambia

The uncertainty in climate change and high water demand pose pressure on the natural water resources supply. Not only does this require better understanding but also a call for immediate interventions, mitigation and adaptive measures. This study evaluates catchment water resources in the Luwombwa sub-catchment in Zambia through statistical analysis in the downscaling of past, present and future climatic variables from the CMIP6 climatic model. These variables are then integrated into WEAP - a semi-distributed hydrological and water evaluation model - to perform water demand and allocation scenario modelling. Mult-site calibration and validation were conducted on five selected micro-catchments within Luwombwa sub-catchment. The model performance was assessed usng the R2, NSE and PBIAS as the objective functions. Satisfactory values of 92 % for R2, 82 % for NSE and 6.9 % for PBIAS were achieved. This allowed for scenario modelling on water demand and allocation among competing users. Three future scenarios (2022–2050) were developed from the historical to baseline (1988–2022) and included state of water resources availability under climate change, expansion of irrigation area and impact of dam construction in the sub-catchment. The study reveals a decrease of 20 % in sub-catchment's water availability resulting from 9.3 % (equivalent to 4oC) rise in maximum temperature and 4.5 % reduction in rainfall within the entire sub-catchment. This is especially under the persistence of SSP370 climate variability scenario projections downscaled from four GCM models by the year 2050. The study further revealed that the change point for anticipated future climate extremes is likely to occur between 2027 and 2030. The results are indicative of downward trends in streamflow under climate change and socioeconomic development leading to increase in water value and water scarcity. The insights from the study are critical to inform formulation of effective catchment water resources management strategies such as the development of management plans and adapation measures in the face of climate change and the needs for different stakeholders involvement.

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

AbstractTravel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

Comparing statistical forecasting methods for modelling respiratory virus healthcare demand

Abstract Background Emergency departments in the United Kingdom are under sustained pressure with rising demand for healthcare, gradually decreasing the functionality of the NHS. Forecasts can aid management in the allocation of appropriate healthcare supplies and staffing needs in advance of an expected surge, as well as allow stakeholders to assess most likely outcomes and potential worst-case scenarios. This is especially valuable during the seasonal waves of respiratory infections. Respiratory viruses are usually concentrated in a 6-8 week seasonal surge during the winter period, creating large pressure on the health system. Current forecast models’ performance depreciates significantly after 1 week. This analysis will forecast 6 weeks of respiratory health admissions, comparing traditional and newly emerging forecasting methods. Methods Using 20 years of English hospital admissions data, we evaluated both within season training models (2-20 weeks) and longer term seasonal models (1-10 years), to forecast 6 weeks of hospital admissions. Respiratory admissions were defined according to ARI/ILI ICD10 codes assigned in hospital, stratified by age and region. Models were iteratively constructed, trained and probabilistic forecasts were evaluated across the respiratory season of September to April using Weighted Interval Score. Averages of each model across iterations were generated for comparison. Results For non-seasonal models, shorter model training lengths created better forecast models for 6 weeks. For seasonal models, optimal training length varied across model complexity. Simple models performed best at forecasting 1 week, however performance deteriorated the most as the forecast window increased. Overall, the Prophet model performed best on average at forecasting 6 weeks across all scenarios. Conclusions Applying our framework for optimal model selection in forecasting analysis will help to inform better practice for future health demand modelling. Key messages • This analysis will inform better practice for future health demand forecasting 6 weeks in advance, allowing for preparation of resources for seasonal surges in healthcare demand. • This analysis suggests using models with modest levels of increased complexity over simpler models could significantly improve forecast performance in the short-medium term.

Incorporating policy mixes to multimodal network capacity considering second-best constraints

In the pursuit of capacity-oriented urban sustainable development (SD), this study aims to enhance the multimodal transport network by making it more accessible, efficient, and environmentally friendly. A sustainable framework combining policy mixes and second-best constraints is developed as a multimodal transportation network capacity (TNC) model with the bi-level programming (BLP) formulation. The upper-level model is to maximise the total origin-destination (OD) demand with considering second-best constraints. The lower-level model is a combined mode split and traffic assignment with elastic demand (CMSTA-ED) model which considers policy mixes. Numerical experiments are conducted to demonstrate the effectiveness of the hybrid PSO–GWO algorithm, the synergy of the method for selecting and mixing policy instruments based on the proposed assessment system, and a methodology to establish a policy mix. The findings of this research offer practical guidance for formulating well-informed and no-redundant policy mixes, contributing to the improvement of sustainable multimodal transportation network performance.

Leveraging large language models for daily tourist demand forecasting

ABSTRACT Large Language Models have attracted the attention of tourism researchers, and many discussions have been published in leading tourism journals. However, little research has been conducted on how to use Large Language Models in tourism research. In this paper, we propose a new tourist arrival forecasting model based on the Large Language Model. The Large Language Model is used to extract and produce the satisfaction scores from the review comments by the tourists. The generated satisfaction scores are incorporated into the tourist arrival forecasting model to take full advantage of the extracted information from the review comments. We have applied the Large Language Model based forecasting model to predict the tourist arrival in Macao. Experiment results show that the proposed model has produced forecasts with improved forecasting accuracy.

Seismic risk estimates for reinforced concrete structures with incorporation of corrosion and aftershock

Numerous reinforced concrete (RC) structures are exposed to aggressive environments, such as chloride, mainshock and aftershock. These environmental and extreme loads have the potential to increase the seismic risks during structure's service life. This study introduces a practical probabilistic methodology to estimate the seismic risk of corroded RC frame subjected to mainshock-aftershock sequences. In this methodology, a time-variant modeling strategy is used to simulate the geometrical and mechanical properties of structural degraded materials. The Bayesian updating theorem is employed to calibrate the demand models, which are then used to estimate the fragilities and confidence intervals considering the model uncertainties. A Copula-based approach is conducted to generate joint probability of mainshock and aftershock intensities and the seismic hazard of the mainshock-aftershock scenario. Finally, the seismic risk and associated confidence intervals are estimated by integrating over all mainshock and aftershock intensities levels. A typical corroded RC frame structure is used to illustrate the proposed methodology. The results show that the contribution of corrosion and aftershock would lead a 10 times higher seismic risks compared to scenarios considering only mainshock damage. It is necessary to account for the influence of both corrosion and aftershock in seismic design.

Bayesian inference of initial conditions from non-linear cosmic structures using field-level emulators

ABSTRACT Analysing next-generation cosmological data requires balancing accurate modelling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full N-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from N-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of $1.95\,h^{-1}$ Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency $k_\mathrm{N} \approx 2.79h \,\, \mathrm{Mpc}^{-1}$. Posterior resimulations – using the inferred initial conditions for N-body simulations – show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate $M_{200\mathrm{c}}$ halo mass function and stacked density profiles of haloes in different mass bins $[0.853,16]\times 10^{14}\,{\rm M}_{\odot }\,h^{-1}$. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modelling, indicating the potential for extracting additional information at smaller scales.

Week-long activity-based modelling: a review of the existing models and datasets and a comprehensive conceptual framework

ABSTRACT Activity-based travel demand models emerged mainly to fix the conceptual, statistical, and operational deficiencies of conventional trip-based models. This is done by microstimulating the activity scheduling behaviour of individuals/households instead of modelling the number of trips between the zones of an urban area. In the “Next Generation” of activity-based models (ABMs), researchers are making an effort to improve their capacity to replicate the travel-activity patterns of urban populations more realistically. Expanding the modelling time frame from a single day to an entire week is one of the essential aspects of the “Next Generation” of ABMs. Although there is still a long way to go before a comprehensive and operational week-long ABM can be developed, the literature on its different aspects, the theoretical and conceptual frameworks, and the efforts to collect multi-day travel-activity diaries are now at a stage that is worth a comprehensive and systematic review. Therefore, the current study is devoted to exploring the existing literature on multi-day activity-based modelling, categorising its elements in a systematic manner, searching for the research gaps in the existing models and proposing a comprehensive framework to fill those gaps.